Impact of population ageing on cancer-related disability-adjusted life years: A global decomposition analysis

Background As the global population ages, the burden of cancer is increasing. We aimed to assess the impact of population ageing on cancer-related disability-adjusted life years (DALYs). Methods We used the decomposition method to estimate the impact of ageing, population growth, and epidemiological change on cancer-related DALYs from 1990 to 2019, stratified by 204 countries/territories and by their sociodemographic index (SDI). This approach separates the net effect of population ageing from population growth and change in age-specific DALY rates. Results Cancer-related DALYs among individuals aged ≥65 years increased by 95.14% between 1990 (52.25 million) and 2019 (101.96 million). Population growth was the main contributor to cancer-related DALYs (92.38 million, attributed proportion: 60.91%), followed by population ageing (41.38 million, 27.28%). Cancer-related DALYs attributed to population ageing followed a bell-shaped pattern when stratified by SDI, meaning they peaked in middle-SDI countries. Cancer-related DALYs attributed to ageing increased in 171 and decreased in 33 countries/territories. The top three cancer types with the highest increase in the absolute number of cancer-related DALYs associated with ageing were tracheal, bronchus, and lung (8.72 million); stomach (5.06 million); and colorectal (4.28 million) cancers, while the attributed proportion of DALYs was the highest in prostate (44.75%), pancreatic (40.93%), and non-melanoma skin (38.03%) cancers. Conclusions Population ageing contributed to global cancer-related DALYs, revealing a bell-shaped pattern when stratified by socioeconomic development, affecting middle-SDI countries the most. To respond to the growing ageing population and reduce cancer-related DALYs, it is necessary to allocate health care resources and prioritize interventions for older adults.


Table S2
Checklist of information that should be included in new reports of global health estimates ....

Table S3
The number (100000) and proportion of cancer

Text S1 The decomposition method
All decomposition methods for absolute numbers attribute differences or changes in total DALYs to the changes in various components or factors, such as population size, age structure, and changes in DALY rates.Compared to the two most commonly used decomposition methods, the new decomposition method we adopted in this study was reported to be robust to the choice of decomposition order of the three factors and the choice of the reference group [1].
Using the difference in total DALYs in 1990 and 2019 for the world, we demonstrate the calculation of DALYs attributed to the three factors.Age was divided using 5-year increments, from 15 years old to 94 years.Let dij, nij, mij, and sij denote the number of DALYs, population size, age-specific DALY rates, and proportion of population for the i th age group of the j th year, respectively, (i = 1, 2, …, 16; j = 1, 2).Let D1 and D2, N1 and N2, M1 and M2 represent the total number of DALYs, population size, and crude DALY rate for years 1990 and 2019, respectively.

Meaning of mathematical symbols in the decomposition formula
Using Mp, Ma, and Mm to represent the main effects of the changes in population size, age structure, and DALY rates, and Ipa, Ipm, Iam, and Ipam to represent their two-way and three-way interactions, respectively.These terms are calculated as follows when using the year 1990 as the reference: Using the year 2019 as the reference, the formulas are calculated as follows: The contribution of each factor includes its main effect and partial interactions with other factors.
(1) Suppose a%, b%, and c% of the two-way interaction between population size and age structure, population size and DALY change, and age structure and DALY change are allocated to the first factor, respectively.Accordingly, (100-a)%, (100-b)% and (100-c)% of the three two-way interactions are allocated to the second factor.
Using  ( ′ ),  ( ′ ) and  ( ′ ) to represent the number of DALYs attributed to age structure, DALY change, and population size defined by the method when using the year 1990 (year 2019) as a reference, the contributions of the three factors can be calculated as follows: The decomposition results should remain unchanged in absolute value when the reference population changes, so we have a group of three equations: Through formula derivation, we have three simplified equations: These three equations cannot always be true unless a, b, and c all equal 50.
The three equations have no requirements for d1 and d2.Given that there is no theoretical guidance for allocating the three-way interaction of three factors, we divide it equally, d1=d2=⅓×100.
The contributions of the three factors can be calculated as follows:  =   + ½  + ½  + ⅓   =   + ½  + ½  + ⅓   =   + ½  + ½  + ⅓  Thus, A represents the effect of changes in age structure.Because the proportion of older age groups has been reported to increase recently for most countries [2], the impact of age structure represents the effect of population aging [3][4][5][6].

Table S2 Checklist of information that should be included in new reports of global health estimates
Item #

Checklist item
Reported on page # Objectives and funding 1 Define the indicator(s), populations (including age, sex, and geographic entities), and time period(s) for which estimates were made.
Methods-paragraph 1 and 2 List the funding sources for the work.

No funding Data Inputs
For all data inputs from multiple sources that are synthesized as part of the study: 3 Describe how the data were identified and how the data were accessed.Methods-paragraph 1 4 Specify the inclusion and exclusion criteria.Identify all ad-hoc exclusions.
No data was excluded (Methods-paragraph 2) 5 Provide information on all included data sources and their main characteristics.For each data source used, report reference information or contact name/institution, population represented, data collection method, year(s) of data collection, sex and age range, diagnostic criteria or measurement method, and sample size, as relevant.

6
Identify and describe any categories of input data that have potentially important biases (e.g., based on characteristics listed in item 5).
No such data (Methods-paragraph 1) For data inputs that contribute to the analysis but were not synthesized as part of the study: 7 Describe and give sources for any other data inputs.All data were derived from GBD 2019 (Methods-paragraph 1 and 2) For all data inputs: 8 Provide all data inputs in a file format from which data can be efficiently extracted (e.g., a spreadsheet rather than a PDF), including all relevant meta-data listed in item 5.For any data inputs that cannot be shared because of ethical or legal reasons, such as thirdparty ownership, provide a contact name or the name of the institution that retains the right to the data.

Data analysis 9
Provide a conceptual overview of the data analysis method.A diagram may be helpful.

Methods-paragraph 4 and S1 Text 10
Provide a detailed description of all steps of the analysis, including mathematical formulae.This description should cover, as relevant, data cleaning, data pre-processing, data adjustments and weighting of data sources, and mathematical or statistical Methods-paragraph 4,5 6 and 7 model(s).

11
Describe how candidate models were evaluated and how the final model(s) were selected.
Only one method was used in this study (Methods-paragraph 4) 12 Provide the results of an evaluation of model performance, if done, as well as the results of any relevant sensitivity analysis.
The robust of the method was evaluated in a other study (S1 Text) 13 Describe methods for calculating uncertainty of the estimates.State which sources of uncertainty were, and were not, accounted for in the uncertainty analysis. Discussion-limitations

14
State how analytic or statistical source code used to generate estimates can be accessed.
No specific software was needed for the estimation.

Results and Discussion 15
Provide published estimates in a file format from which data can be efficiently extracted.

Discussion-limitations 17
Interpret results in light of existing evidence.If updating a previous set of estimates, describe the reasons for changes in estimates.

18
Discuss limitations of the estimates.Include a discussion of any modelling assumptions or data limitations that affect interpretation of the estimates.

Figure S1 Figure
Figure S1 The forecast of the number (100000) cancer-related DALYs attributed to population aging globally by 2030.

Figure S2 Figure
Figure S2